Roles & Responsibilities: 

Operational 

1. Serve as project lead on most types of analyses performed by the group – develop project plans and manage work efforts of supporting team members 
2. Assess client business and marketing challenges, as well as data assets, and recommend the most appropriate analytic methodology to meet client goals 
3. Manage multiple projects and client accounts simultaneously, while serving as the primary point of contact with the Epsilon account team and clients 4. Monitor project resources and track progress against task plans to ensure on-time and on-budget project delivery 
5. Document communication with clients to manage expectations and negotiate deadlines 
6. Present analytic findings and statistical results to account teams and clients 
7. Participate in project design, planning and execution, and provide pre-sales support and run proof of concept (POC) for account / sales teams 
8. Ability to perform hands-on analysis on large volumes of customer’s first-party data and third-party data to provide statistically valid analysis and related outputs 
9. Design and Implement Machine learning models using Spark ML and Python/Pyspark 
10. Own end to end implementations of Marketing machine learning models such as Customer Segmentation, Propensity models, CLTV etc. 
11. Create engaging and meaningful data visualizations / power point presentations of findings linked to clear client business impact. 

Functional 

1. Lead, manage, mentor and train a team of 10+ Data Scientists to implement the organizations vision and the roadmap with the help of the team members 
2. Provide thought leadership to improve existing processes and incorporate new processes to help team deliver exceptional analytical solutions 
3. Ensure that your team is engaged and motivated to excel in their roles and are being productively utilized on billable projects 
4. Guide your team to follow all policies and procedures for programming, project documentation and other processes as defined by the organisation 
5. Become proficient with Epsilon data assets 
6. Be an active learner - learn new and state of the art tools, technologies, algorithms and methodologies to be at the edge of data science learnings 
7. Collaborate well with cross-functional internal teams as needed to drive results 

Qualifications 

1. Bachelor’s degree, or higher, in a quantitative discipline (e.g., Engineering, Statistics, Economics, Mathematics, Marketing Analytics) or significant relevant coursework 
2. Minimum 10+ years of experience in analytics and data science; marketing analytics experience preferred 
3. Must have experience managing a team of people including their goal / outcome setting, performance management and feedback communication 
4. Strong experience with Python, SQL and Microsoft office packages 
5. Must have hands-on experience with machine learning and statistical techniques like Logistic regression, Decision trees, Random Forest, K-means clustering etc. 
6. Experience in distributed computing using PySpark, comfort with DataBricks and experience in machine learning with Spark / MLlib 
7. Strong analytic thought process and ability to interpret findings 
8. Acute attention to detail (QA/QC) 
9. Excellent verbal and written communication skills and good at developing relationships across cross-functional teams 1
0. Highly motivated and collaborative team player with strong interpersonal skills 
11. Effective organization and time management skills 
12. Solid planning, priority setting, and project management skills with experience managing multiple projects and resources concurrently 
13. Passion for understanding key business problems, bringing together a team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experiences 
14. Proven capability to deliver end-to-end analyses by asking the right questions, extracting data, and building predictive models to ensure actionable results 
15. Ability to display data visually, creating powerful presentations which effectively demonstrate the value of analytic deliverables